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Increased ice shelf melting caused by ocean warming in the Amundsen Sea is likely committed for the coming century. However, the drivers behind this projected ocean warming are not yet fully understood. Using a high‐resolution regional model, we compare future projections of the Amundsen Sea under the RCP8.5 scenario against pre‐industrial projections. The two ensembles differ measurably between 2013 and 2018, and continue to diverge under high‐emissions forcing. We conduct two more experiments separating the effects of stronger, poleward‐shifted winds against a warmer, wetter atmosphere (defined here as atmospheric thermodynamics). We run experiments that use RCP8.5 winds and pre‐industrial thermodynamics, and vice versa. We find that atmospheric thermodynamic change modulates Circumpolar Deep Water inflow onto the shelf, making thermodynamic change the primary driver of ocean warming on the continental shelf on centennial timescales.

Salinity is often considered one of the most influential environmental factors affecting estuarine communities. This is particularly true for low-inflow estuaries, which typically experience limited freshwater input for long periods of time. Disturbance events related to hypersalinity (> 35) occur commonly in Baffin Bay, TX, and have been linked to changes in fish and macrofauna abundance. Here, we use a long-term fishery-independent dataset collected by the Texas Parks and Wildlife Department (1983–2019) to determine the extent to which salinity affects the fish and invertebrate communities (i.e., species richness, spatial distribution, and community composition) in the Baffin Bay complex. Increased salinity had a negative effect on species richness of communities captured in two gear types (gill net and bag seine) and across seasons (fall and spring) based on generalized linear models. Species richness was also explored spatially using optimized hot spot analyses. The location of hot and cold spots of species richness varied within each salinity bin (< 35, 35–50, > 50) for both bag seine and gill net samples. However, more well-defined hot spots were apparent where salinity was > 50. Multivariate analyses used to explore changes in community composition across salinity bins revealed differences between salinity regimes for both gear types. SIMPROF tests also yielded groups of species with similar responses in abundance across salinity bins, which could be used to identify indicator species. These results could aid resource managers as they incorporate environmental factors such as salinity into ecosystem-based fisheries management strategies. In addition, understanding drivers of community composition and species distribution will be needed when responding to extreme hypersalinity events.

Low-inflow estuaries are naturally more susceptible to anthropogenic stressors, compared to well flushed systems, with excessive nutrient loading posing a particular threat. This study investigated the benthic diatom community structure of two eutrophic, microtidal estuaries impacted by daily wastewater effluent discharges. It was hypothesised that the community structure would be similar between the warm-temperate Hartenbos and subtropical uThongathi estuaries due to disproportionally high dissolved inorganic nitrogen (H: 38 kg DIN d−1; uT: 67.5 kg DIN d−1) and phosphorus (H: 22 kg DIP d−1; uT: 29.7 kg DIP d−1) inputs from wastewater treatment works (WWTWs). Taxa tolerant of high nutrient conditions proliferated in both systems. However, the dominant taxa differed with the brackish Halamphora coffeaeformis species occurring in the Hartenbos Estuary and the freshwater Navicula rostellata, Sellaphora pupula and Navicula gregaria species in the uThongathi Estuary. The overall benthic diatom diversity in both systems was low (H’ > 0 but ≤ 1.5) and indicative of a degraded health state. Temporal differentiation driven by salinity was evident in the Hartenbos Estuary, while changes in community structure were limited to periods of increased river inflow in the uThongathi Estuary. Therefore, while the trophic status of the dominant taxa was determined by the nutrient stress (primary stressor), changes in salinity and river inflow (secondary stressors) shaped the distinct community assemblages observed in each estuary. This study provides insight into the impact of similar anthropogenic-induced pressures in different biogeographical regions and the importance of managing towards a natural dynamic state of microtidal, low-inflow estuaries.

As a result of global climate change, sustainable water supply management is becoming increasingly difficult. Dams and reservoirs are key tools for controlling and managing water resources; they have benefited human cultures in a variety of ways, including enhanced human health, increased food production, water supply for domestic and industrial use, economic growth, irrigation, hydro-power generation, and flood control. This study aims to compare the application of deep learning and conventional machine learning algorithms for predicting daily reservoir inflow. Long short-term memory (LSTM) has been applied as a deep learning algorithm and boosted regression tree (BRT) has been implemented as a machine learning algorithm. Five statistical indices have been selected to evaluate the performance of the proposed models. The selected statistical measurements are mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), Nash Sutcliffe Model Efficiency Coefficient (NSE), and the RMSE-observations standard deviation ratio (RSR). The findings showed that LSTM outperformed BRT with a significant difference in terms of accuracy.

Daily inflow forecasts provide important decision support for the operations and management of reservoirs. Accurate and reliable forecasting plays an important role in the optimal management of water resources. Numerous studies have shown that decomposition integration models have good prediction capacity. Considering the nonlinearity and unsteady state of daily incoming flow data, a hybrid model of adaptive variational mode decomposition (VMD) and bidirectional long- and short-term memory (Bi-LSTM) based on energy entropy was developed for daily inflow forecast. The model was analyzed using the mean absolute error (MAE), the root means square error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), and correlation coefficient (r). A historical daily inflow series of the Baozhusi Hydropower Station, China, is investigated by the proposed VMD-BiLSTM with hybrid models. For comparison, BP, GRNN, ELMAN, SVR, LSTM, Bi-LSTM, EMD-LSTM, and VMD-LSTM, were adopted and analyzed for evaluation and analyzed. We found that the proposed model, with MAE = 38.965, RMSE = 64.783, and NSE = 95.7%, was superior to the other models. Therefore, the hybrid model is robust and efficient for forecasting highly nonstationary and nonlinear streamflow. It can be used as the preferred data-driven tool to predict the daily inflow flow, which can ensure the safe operation of hydropower stations in reservoirs. As an interdisciplinary field spanning both machine learning and hydrology, daily inflow forecasting can become an important breakthrough in the application of deep learning to hydrology.

Many of the world's saline lakes have been shrinking due to consumptive water use. The Great Salt Lake, USA, provides an example for how the health of and ecosystem services provided by saline lakes can be sustained. Many of the world's saline lakes are shrinking at alarming rates, reducing waterbird habitat and economic benefits while threatening human health. Saline lakes are long-term basin-wide integrators of climatic conditions that shrink and grow with natural climatic variation. In contrast, water withdrawals for human use exert a sustained reduction in lake inflows and levels. Quantifying the relative contributions of natural variability and human impacts to lake inflows is needed to preserve these lakes. With a credible water balance, causes of lake decline from water diversions or climate variability can be identified and the inflow needed to maintain lake health can be defined. Without a water balance, natural variability can be an excuse for inaction. Here we describe the decline of several of the world's large saline lakes and use a water balance for Great Salt Lake (USA) to demonstrate that consumptive water use rather than long-term climate change has greatly reduced its size. The inflow needed to maintain bird habitat, support lake-related industries and prevent dust storms that threaten human health and agriculture can be identified and provides the information to evaluate the difficult tradeoffs between direct benefits of consumptive water use and ecosystem services provided by saline lakes.

Reservoir modeling and inflow forecasting has a vital role in water resource management/controlling. Hydrological systems’ complex nature and problems in their application process have prompted researchers to look for more efficient reservoir inflow forecasting methods; hence, the development of artificial intelligence-based techniques in recent years has caused the hybrid modeling to become popular among hydrologists. To this end, effort has been made in the present study to develop a hybrid model that combines a Long-Short Term Memory (LSTM) algorithm—a special recurrent neural network—with a Convolutional Neural Network (CNN) algorithm for the reservoir inflow forecasting. To forecast the flow data, use was made of the support vector machines (SVM), Long Short-Term Memory (LSTM) algorithm, adaptive neuro-fuzzy inference system (ANFIS), Variable Infiltration Capacity (VIC) and autoregressive integrated moving average (ARIMA) model plus the data collected from the flow measurement stations of Doroodzan Dam reservoir in “Kor”—an important river in Fars Province, Iran. The model estimation results were evaluated by the RMSE, MAE, MAPE, MSE and R2 statistical criteria and showed that the hybrid CNN-LSTM method was the most successful model by achieving R2 ≈ 0.9278 (the highest).

During the last decades, the endorheic Lake Urmia basin in northwestern Iran has suffered from declining groundwater tables and a very strong recent reduction in the volume of Lake Urmia. For the case of Lake Urmia basin, this study explores the value of different locally and globally available observation data for adjusting a global hydrological model such that it can be used for distinguishing the impacts of human water use and climate variations. The WaterGAP Global Hydrology Model (WGHM) was for the first time calibrated against multiple in situ and spaceborne data to analyze the decreasing lake water volume, lake river inflow, loss of groundwater, and total water storage in the entire basin during 2003–2013. The calibration process was done using an automated approach including a genetic algorithm (GA) and non-dominated sorting genetic algorithm II (NSGA-II). Then the best-performing calibrated models were run with and without considering water use to quantify the impact of human water use. Observations encompass remote-sensing-based time series of annual irrigated areas in the basin from MODIS, monthly total water storage anomaly (TWSA) from GRACE satellites, and monthly lake volume anomalies. In situ observations include time series of annual inflow into the lake and basin averages of groundwater level variations based on 284 wells. In addition, local estimates of sectoral water withdrawals in 2009 and return flow fractions were utilized. Calibration against MODIS and GRACE data alone improved simulated inflow into Lake Urmia but inflow and lake volume loss were still overestimated, while groundwater loss was underestimated and seasonality of groundwater storage was shifted as compared to observations. Lake and groundwater dynamics could only be simulated well if calibration against groundwater levels led to an adjustment of the fractions of human water use from groundwater and surface water. Thus, in some basins, globally available satellite-derived observations may not suffice for improving the simulation of human water use. According to WGHM simulations with 18 optimal parameter sets, human water use was the reason for 52 %–57 % of the total basin water loss of about 10 km3 during 2003–2013, for 39 %–43 % of the Lake Urmia water loss of about 8 km3, and for up to 87 %–90 % of the groundwater loss. Lake inflow was 39 %–45 % less than it would have been without human water use. The study shows that even without human water use Lake Urmia would not have recovered from the significant loss of lake water volume caused by the drought year 2008. These findings can support water management in the basin and more specifically Lake Urmia restoration plans.

Low-inflow estuaries on the arid, subtropical Texas coast are often subject to oscillations between dry and wet climate cycles, interspersed with stochastic weather events such as prolonged drought, storms, or hurricanes. Sporadic river inflow influences the sources and composition of estuarine particulate organic matter (POM). Shifting proportions of “high quality” POM, consisting of fresh, reactive, labile material, relative to “low quality,” recalcitrant, less available fraction, may lead to ecosystem-wide changes. Between 2012 and 2020, the Mission-Aransas Estuary (MAE) experienced several significant climatic events: severe drought, flooding, and category 4 Hurricane Harvey, ultimately transitioning from a dry to wet climate regime. To assess changes in POM quantity and quality in response to these events, we quantified particulate organic carbon (POC), nitrogen (PN), natural stable isotope abundance (δ13C, δ15N), chlorophyll a, pheophytin, and pheophorbide, and total hydrolyzable amino acids (THAAs) from an 8-year (2012 to 2020) timeseries. Using a multivariate statistical approach, we constructed a degradation index to demonstrate that POM was more degraded during drought and less degraded during the wet period. Average POC, PN, and THAA concentrations increased 66%, 88%, and 88% during the wet period summers and with the degradation index demonstrate elevated proportions of high-quality, bioavailable POM seasonally, and over a climate regime shift. Low-inflow estuaries are useful examples of climate change impacts on systems increasingly stressed by freshwater inflow reduction. The quality shift of POM may play an important role in determining processing rate by bacteria or higher trophic levels, thus affecting ecosystem interactions and functions.

Global warming is considered a major threat to Earth’s lakes water budgets and quality. However, flow regulation, over-exploitation, lack of hydrological data, and disparate evaluation methods hamper comparative global estimates of lake vulnerability to evaporation. We have analyzed the stable isotope composition of 1257 global lakes and we find that most lakes depend on precipitation and groundwater recharge subsequently altered by catchment and lake evaporation processes. Isotope mass-balance modeling shows that ca. 20% of water inflow in global lakes is lost through evaporation and ca. 10% of lakes in arid and temperate zones experience extreme evaporative losses >40 % of the total inflow. Precipitation amount, limnicity, wind speed, relative humidity, and solar radiation are predominant controls on lake isotope composition and evaporation, regardless of the climatic zone. The promotion of systematic global isotopic monitoring of Earth’s lakes provides a direct and comparative approach to detect the impacts of climatic and catchment-scale changes on water-balance and evaporation trends. An isotope synthesis of 1257 global lakes revealed on average 20% of inflow is lost to evaporation, but 10% of Earth’s lakes show extreme evaporative losses. Stable water isotope monitoring is an effective way to detect comparative climatic and catchment-scale impacts on lake water-balance budgets.